Comparative analysis of the global innovation index combining the graph visualization and topological data analysis approaches by Rolando Ismael Yépez, Maricela Fernanda Ormaza

“…The results reveal evolutionary patterns in the global innovation ecosystem. With ε = 0.3, connected components increase from 13 to 14 between 2022 and 2023, reflecting fragmentation that captures heterogeneity in innovation levels, with innovation islands such as Switzerland, United States, and Sweden appearing isolated from developing economies. …”
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Land-use induced changes in extreme temperature predominantly influenced by downward longwave radiation by Meng Zhang, Yanhong Gao, Mingfang Ting, Yan Yu, Guoyin Wang

“…Land use change can modify surface energy balance through land-atmosphere firstly, thereby regulate spatial patterns of water vapor and cloud cover in the Northern Hemisphere through teleconnection. …”
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Projected Emergence Seasons of Year‐Maximum Near‐Surface Wind Speed by Yue Yu, Zhibo Li, Zixiang Yan, Huishuang Yuan, Cheng Shen

“…Our analysis reveals a rapid response of synoptic‐scale extreme NSWS to global warming, with consistent spatial patterns observed across various periods and warming scenarios. …”
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Local Fractional Modeling of Microorganism Physiology Arising in Wastewater Treatment: Lawrence–McCarty Model in Cantor Sets by Yiming Wang, Yiying Feng, Xiurong Xu, Shoubo Jin

“…Compared with classical models, the FLMM’s fractional-order structure provides a more flexible framework to represent memory effects and spatial heterogeneity in microbial communities. These advances establish a mathematical foundation for future experimental validation and suggest potential improvements in predicting nonlinear biomass accumulation patterns.…”
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Design of an intelligent optimization system for high-altitude photovoltaic power plant output power prediction and enhancement using GVSAO-CNN-BiGRU-Attention by Shuai Yuan, Hui Zhou, Kai Zhao

“…The system integrates convolutional neural networks (CNN), bi-directional gated recurrent units (BiGRU), attention mechanisms, and genetic algorithm optimization (GVSAO). The CNN extracts spatial features, while the BiGRU enhances temporal dependency modeling, effectively capturing short- and long-term patterns. …”
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Analysis of behavioral differentiation in smart cities based on mobile users’ usage detail record data by Xianlong Zhao, Xianze Xu, He Nai, Chen Zhou, Zhiyi Hu, Yi Zhang, Hao Jiang

“…Usage detail records not only include plentiful spatial–temporal information, but also describe users’ activities in content space and time. …”
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Large disagreements in estimates of urban land across scales and their implications by TC Chakraborty, Zander S. Venter, Matthias Demuzere, Wenfeng Zhan, Jing Gao, Lei Zhao, Yun Qian

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Driving Risk Affected Areas and Distribution Function of Sharp Horizontal Curves of Expressway by Xiao-fei Wang, Xin-wei Li, Ying Yan, Xin-sha Fu

“…According to the results of the test of the operating speed, the heart rate change of drivers, and the vehicle acceleration, the distribution patterns of driving risks on the certain SHCs were theoretically analyzed. …”
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Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection by Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen, Monica Rosselli, Kelley L. Jackson

“…This paper proposes a quad-tree based approach to the QBDC problem by analyzing driving patterns using a real-world dataset. We propose a geo-regional quad-tree structure to capture the spatial hierarchy of driving trajectories and introduce new driving features representation for input into a convolutional neural network (CNN) for driver classification. …”
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A Comparison of the Effects of Climate and Human Variability on the Thermal Resistance of Clothing by Ferenc Ács, Zsófia Szalkai, Erzsébet Kristóf, Annamária Zsákai

“…Our most important results are the following. (1) The macroscale pattern of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><msub><mrow><mi>r</mi></mrow><mrow><mi>c</mi><mi>l</mi></mrow></msub></mrow><mo>¯</mo></mover></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>∆</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>c</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> fields are very similar, based on which it can be stated that human variability does not modify the spatial distribution of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><msub><mrow><mi>r</mi></mrow><mrow><mi>c</mi><mi>l</mi></mrow></msub></mrow><mo>¯</mo></mover></mrow></semantics></math></inline-formula>. (2) The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>∆</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>c</mi><mi>l</mi></mrow></msub></mrow></semantics></math></inline-formula> values are roughly a quarter of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><msub><mrow><mi>r</mi></mrow><mrow><mi>c</mi><mi>l</mi></mrow></msub></mrow><mo>¯</mo></mover></mrow></semantics></math></inline-formula> values. …”
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Bottom trawling and environmental variables drive the biodiversity of mediterranean demersal assemblages by Davide Agnetta, Ivano Vascotto, Diego Panzeri, Igor Celić, Cosimo Solidoro, Tomaso Fortibuoni, Saša Raicevich, Simone Libralato

“…Assessing how factors affect spatial and temporal patterns of biodiversity is an essential task for the Ecosystem Based Management approach. …”
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Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition by Peiwang Zhu, Chengwu Liang, Yalong Liu, Songqi Jiang

“…Secondly, an innovative multi-scale spatial–temporal feature interaction module is proposed to jointly learn sub-action dependencies and facilitate skeletal motion interactions, where long-range motion patterns are embedded to enhance correlation modeling. …”
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Radar Compound Jamming Recognition Based on Image Segmentation and Fused Attention Residual Network by Peishan Li, Jian Yang, Jiaao Lin

“…Compound jamming is difficult to recognize due to a wide array of diverse compound patterns. To address this issue, this study proposes a method for the segmentation and recognition of compound jamming signals. …”
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Soil erosion estimation and risk assessment based on RUSLE in Google Earth Engine (GEE) in Turkiye by Endalamaw Dessie Alebachew, Wudu Abiye, Orhan Dengiz, İnci Demirağ Turan

“…Our analysis provides spatial insights into erosion patterns across the region, revealing that soil loss rates range from 0–5 t/ha/yr in stable areas to over 200 t/ha/yr in severely eroded regions. …”
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Multifaceted changes in water availability with a warmer climate by Binglan Gu, Sha Zhou, Bofu Yu, Kirsten L. Findell, Benjamin R. Lintner

“…Abstract Climate warming alters spatial and seasonal patterns of surface water availability (P-E), affecting runoff and terrestrial water storage. …”
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An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions by Ravi Raj, Andrzej Kos

“…CNNs are developed to autonomously and effectively acquire spatial patterns of characteristics using backpropagation, leveraging an array of elements, including convolutional layers, pooling layers, and fully connected layers. …”
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Peak amplitude dependence of radiation properties of nonlinear Thomson scattering under an applied magnetic field by Xiaotian Gong, Feiyang Gu, Youwei Tian

“…A framework is established for the analysis of electron dynamics, spatial patterns and spectral distribution in combined fields of tightly focused circularly polarised Hermite-Gaussian lasers (a0 = 1–20) and magnetic fields (B0 = 0.5 kT). …”
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Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty by Shirali Kadyrov, Azamkhon Azamov, Yelbek Abdumajitov, Cemil Turan

“…Traditional methods often fail to adapt to real-time uncertainties in customer demand and traffic patterns or scale to large problem instances. In this work, we propose a deep reinforcement learning framework to learn adaptive routing policies for dynamic capacitated vehicle routing problem environments with stochastic demand and traffic. …”
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Covid-19 pandemic data analysis using tensor methods by Dipak Dulal, Ramin Goudarzi Karim, Carmeliza Navasca

“…First, we use tensor models, canonical polyadic, and higher-order Tucker decompositions to extract patterns over multiple modes. Second, we implement a tensor completion algorithm using canonical polyadic tensor decomposition to predict spatiotemporal data from multiple spatial sources and to identifyCovid-19 hotspots. …”
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Masked Convolutions Within Skip Connections for Video Anomaly Detection by Demetris Lappas, Vasileios Argyriou, Dimitrios Makris

“…The proposed methodology is evaluated on popular video datasets, showcasing its effectiveness in capturing intricate patterns and contexts. The results highlight the superiority of the modified UNet with MaskedConv3D layers compared to traditional approaches. …”
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